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models bert base uncased
Description: BERT is a pre-trained model in the field of NLP (natural language processing) released by Google. It is an AI language model that has been trained on a large corpus of English data using a self-supervised method, learning to predict masked words in a sentence and to predict if two sentences are consecutive or not. This model comes in several variations, including an English "uncased" version, Chinese and multilingual cased/uncased versions, and 24 smaller models. It is primarily used to fine-tune on downstream tasks for NLP, such as sequence classification, token classification, or question answering, although it can also be used for masked language modeling and next sentence prediction. The model was trained on BookCorpus and English Wikipedia data and its training procedure involved preprocessing the data, tokenizing it, and masking 15% of the tokens. The BERT model is fine-tuned on various tasks, and its test results on these tasks have shown impressive accuracy.
Please Note: This model accepts masks in [mask] format. See Sample input for reference. > The above summary was generated using ChatGPT. Review the original model card to understand the data used to train the model, evaluation metrics, license, intended uses, limitations and bias before using the model. ### Inference samples Inference type|Python sample (Notebook)|CLI with YAML |--|--|--| Real time|fill-mask-online-endpoint.ipynb|fill-mask-online-endpoint.sh Batch |fill-mask-batch-endpoint.ipynb| coming soon ### Finetuning samples Task|Use case|Dataset|Python sample (Notebook)|CLI with YAML |--|--|--|--|--| Text Classification|Emotion Detection|Emotion|emotion-detection.ipynb|emotion-detection.sh Token Classification|Named Entity Recognition|Conll2003|named-entity-recognition.ipynb|named-entity-recognition.sh Question Answering|Extractive Q&A|SQUAD (Wikipedia)|extractive-qa.ipynb|extractive-qa.sh ### Model Evaluation Task|Use case|Dataset|Python sample (Notebook)|CLI with YAML |--|--|--|--|--| Fill Mask|Fill Mask|rcds/wikipedia-for-mask-filling|evaluate-model-fill-mask.ipynb|evaluate-model-fill-mask.yml ### Sample inputs and outputs (for real-time inference) #### Sample input json { "inputs": { "input_string": ["Paris is the [MASK] of France.", "Today is a [MASK] day!"] } } #### Sample output json [ { "0": "capital" }, { "0": "beautiful" } ]
Version: 8
Preview computes_allow_list : ['Standard_NV12s_v3', 'Standard_NV24s_v3', 'Standard_NV48s_v3', 'Standard_NC6s_v3', 'Standard_NC12s_v3', 'Standard_NC24s_v3', 'Standard_NC24rs_v3', 'Standard_NC6s_v2', 'Standard_NC12s_v2', 'Standard_NC24s_v2', 'Standard_NC24rs_v2', 'Standard_NC4as_T4_v3', 'Standard_NC8as_T4_v3', 'Standard_NC16as_T4_v3', 'Standard_NC64as_T4_v3', 'Standard_ND6s', 'Standard_ND12s', 'Standard_ND24s', 'Standard_ND24rs', 'Standard_ND40rs_v2', 'Standard_ND96asr_v4'] license : apache-2.0 model_specific_defaults : ordereddict([('apply_deepspeed', 'true'), ('apply_lora', 'true'), ('apply_ort', 'true')]) task : fill-mask
View in Studio: https://ml.azure.com/registries/azureml/models/bert-base-uncased/version/8
License: apache-2.0
SHA: 0a6aa9128b6194f4f3c4db429b6cb4891cdb421b
datasets: bookcorpus, wikipedia
evaluation-min-sku-spec: 2|0|7|14
evaluation-recommended-sku: Standard_DS2_v2
finetune-min-sku-spec: 4|1|28|176
finetune-recommended-sku: Standard_NC24rs_v3
finetuning-tasks: text-classification, token-classification, question-answering
inference-min-sku-spec: 2|0|7|14
inference-recommended-sku: Standard_DS2_v2
languages: en